Introduction

The OpenFlow Protocol defines a standardized, extensible, and model-agnostic specification for orchestrating AI workflows using structured JSON definitions. It serves as the formal foundation for the OpenFlow SDK, enabling consistent communication and execution of large language model (LLM)-driven tasks across diverse environments and providers.

At its core, the protocol abstracts AI workflows into declarative JSON objects composed of nodes, variables, inputs, outputs, and execution rules. This structure allows developers to design, share, and execute AI workflows in a way that is portable, inspectable, and scalable — independent of the specific AI backend or toolchain used.

Design Principles

Principle	Description	Benefits	Implementation
Declarative Nature	Workflows defined as data structures	Easier to understand, validate, and maintain	JSON schema-based definitions
Composability	Build complex workflows from simple components	Reusable components, modular design	Node-based architecture
Vendor Neutrality	No dependency on specific AI providers	Avoid vendor lock-in, flexible integration	Abstract provider interface
Type Safety	Strong typing for inputs, outputs, variables	Catch errors early, better tooling	JSON Schema validation
Deterministic Execution	Predictable behavior across implementations	Reliable results, easier debugging	Standardized execution model
Resource Efficiency	Optimized for concurrent execution	Higher throughput, cost efficiency	Rate limiting, connection pooling
Extensibility	Support for custom nodes and providers	Adapt to specific needs	Plugin architecture

Purpose of the Protocol

The main goals of the OpenFlow Protocol are:

Standardization: Establish a universal JSON schema for defining and executing AI workflows, reducing fragmentation across tools and platforms.
Interoperability: Enable seamless integration with different LLM providers, vector databases, plugins, and execution engines via a consistent protocol.
Modularity: Allow workflows to be composed of pluggable tools, conditional logic, iterations, and model switches.
Auditability: Make LLM executions traceable and inspectable by defining every step and input explicitly.
Portability: Ensure that workflows can be moved across environments (cloud, local, edge) without changing their structure or logic.
Extensibility: Support extensions and custom tools that can be registered and used within the protocol-defined flows.

By adopting the OpenFlow Protocol, developers and organizations can move beyond one-off scripts or proprietary LLM integrations, and toward reusable, composable, and maintainable AI systems that are future-proof and transparent.

Architecture

Layer	Responsibility	Components	Interfaces
Flow Layer	Workflow definition and metadata	Flow schema, variables, node sequences	Flow definition JSON
Node Layer	Individual processing operations	LLM, embedding, vector, utility nodes	Node configuration interface
Provider Layer	External service abstraction	LLM, vector DB, embedding providers	Provider API abstraction
Execution Layer	Runtime processing environment	Flow executor, concurrency control	Execution interface
Validation Layer	Schema and dependency validation	JSON schema, dependency analyzer	Validation interface

Communication Model

OpenFlow follows a push-based execution model where:

Flows are submitted to an executor with optional input variables
The executor validates the flow against protocol schemas
Nodes execute sequentially with dependency resolution
Variables are resolved just-in-time during execution
Results are collected and returned as structured output

Protocol Compliance